{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#Notebooker Test!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "plots = 5\n",
    "days = 100\n",
    "start_date = \"2020-01-01\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -\n",
    "arr = np.random.rand(days, plots) - 0.5\n",
    "dts = np.array(start_date, dtype=np.datetime64) + np.arange(days)\n",
    "df = pd.DataFrame(arr, index=dts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -\n",
    "df.cumsum().plot()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_json": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
